dc.contributor.author |
Vlassis, NA |
en |
dc.contributor.author |
Papakonstantinou, G |
en |
dc.contributor.author |
Tsanakas, P |
en |
dc.date.accessioned |
2014-03-01T02:41:32Z |
|
dc.date.available |
2014-03-01T02:41:32Z |
|
dc.date.issued |
1998 |
en |
dc.identifier.uri |
https://dspace.lib.ntua.gr/xmlui/handle/123456789/30516 |
|
dc.subject |
Dynamic Environment |
en |
dc.subject |
Mobile Robot |
en |
dc.subject |
Mobile Robot Localization |
en |
dc.subject |
Position Estimation |
en |
dc.subject |
Probability Density Function |
en |
dc.subject |
Robot Localization |
en |
dc.subject |
Sensor Model |
en |
dc.subject |
Structural Change |
en |
dc.subject.other |
Algorithms |
en |
dc.subject.other |
Mathematical models |
en |
dc.subject.other |
Mobile robots |
en |
dc.subject.other |
Motion planning |
en |
dc.subject.other |
Probability density function |
en |
dc.subject.other |
Sensor data fusion |
en |
dc.subject.other |
Dynamic sensory probabilistic maps |
en |
dc.subject.other |
Mobile robot localization |
en |
dc.subject.other |
Intelligent robots |
en |
dc.title |
Dynamic sensory probabilistic maps for mobile robot localization |
en |
heal.type |
conferenceItem |
en |
heal.identifier.primary |
10.1109/IROS.1998.727276 |
en |
heal.identifier.secondary |
http://dx.doi.org/10.1109/IROS.1998.727276 |
en |
heal.publicationDate |
1998 |
en |
heal.abstract |
In order to localize itself, a mobile robot tries to match its sensory information at any instant against a prior environment model, the map. A probabilistic map can be regarded as a model that stores at each robot configuration q the probability density function of the sensor readings at q. By combining the knowledge of its current position, the new-coming sensory information, and the probabilistic map the robot is capable of improving its prior position estimate. In this paper we propose a novel sensor model and a method for maintaining a probabilistic map in cases of dynamic environments. When the environment structure changes, the map must adapt to this change by modifying the sensor densities at the respective configurations. We propose a combined algorithm for map update and robot localization. |
en |
heal.publisher |
IEEE, Piscataway, NJ, United States |
en |
heal.journalName |
IEEE International Conference on Intelligent Robots and Systems |
en |
dc.identifier.doi |
10.1109/IROS.1998.727276 |
en |
dc.identifier.volume |
2 |
en |
dc.identifier.spage |
718 |
en |
dc.identifier.epage |
723 |
en |